import argparse import os import sys import tqdm import cv2 import torch import scipy.interpolate as spi import numpy as np import plotly.graph_objects as go import plotly.express as ex from torchvision.transforms import Resize from torchvision.transforms.functional import InterpolationMode os.environ["CUDA_VISIBLE_DEVICES"] = "4" sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..') from data.utils import Frame def fill(img, mask): coords = np.transpose(np.stack(np.nonzero((1 - mask))), (1,0)) img_idx = np.transpose(np.stack(np.nonzero((mask))), (1,0)) img_data = img[img_idx[:,0], img_idx[:,1], :] interpolator = spi.NearestNDInterpolator(x=img_idx, y=img_data) # interpolator = spi.RBFInterpolator(y=img_idx, d=img_data, neighbors=5, kernel='linear') img_2 = interpolator(coords) res = np.zeros((512,256,3), dtype=np.float32) res[coords[:,0], coords[:,1], :] = img_2 res[img_idx[:,0], img_idx[:,1], :] = img_data return res def fill_label(img, mask): coords = np.transpose(np.stack(np.nonzero((1 - mask))), (1,0)) img_idx = np.transpose(np.stack(np.nonzero((mask))), (1,0)) img_data = img[img_idx[:,0], img_idx[:,1]] interpolator = spi.NearestNDInterpolator(x=img_idx, y=img_data) img_2 = interpolator(coords) res = np.zeros((512,256), dtype=np.float32) res[coords[:,0], coords[:,1]] = img_2 res[img_idx[:,0], img_idx[:,1]] = img_data return res def generate_downscaled_uv_map(folder, frame: Frame, size): img = np.load(os.path.join(folder, 'uv_mask', f'{frame.id}_{frame.garment}.npz')); img = img['uv_mask'].astype(np.float32) down = resizers_nearest[size](torch.from_numpy(img[None,:,:]).to(device)) down = np.squeeze(down.cpu().numpy().astype(bool)) if not os.path.exists(os.path.join(folder, 'uv_mask' + '_' + str(size))): os.makedirs(os.path.join(folder, 'uv_mask' + '_' + str(size)), exist_ok=True) np.savez_compressed(os.path.join(folder, 'uv_mask' + '_' + str(size), f'{frame.id}_{frame.garment}.npz'), uv_mask = down) class UVMaskStore(): def __init__(self, folder, size): self.store = dict() self.folder = folder self.size = size def read(self, frame: Frame): path = os.path.join(self.folder, 'uv_mask' + ('_' + str(self.size) if self.size != 512 else ''), f'{frame.id}_{frame.garment}.npz') if not os.path.exists(path): generate_downscaled_uv_map(self.folder, frame, self.size) mask = np.load(path) mask = mask['uv_mask'] return mask def __getitem__(self, frame: Frame) -> dict: if (frame.id, frame.garment) not in self.store: self.store[(frame.id, frame.garment)] = self.read(frame) return self.store[(frame.id, frame.garment)] def downscale_uv_maps_posed_body(folder, type, sizes): for size in sizes: os.makedirs(os.path.join(folder, type + str(size)), exist_ok=True) frames = sorted(os.listdir(os.path.join(folder, type)))[int(args.start):int(args.end)] frames = [Frame(frame.split('_')[0], frame.split('_')[1].split('.')[0], '') for frame in frames] for frame in tqdm.tqdm(frames): img = np.load(os.path.join(folder, type, f'{frame.id}_{frame.frame_nr}.npz'))['uv_body_posed'] img = fill(img, uv_masks_body[512]) for size in sizes: mask = uv_masks_body[size] down = resizers[size](torch.from_numpy(img.transpose((2,0,1))).to(device)).cpu().numpy() down = down * mask[None,:,:] np.savez_compressed(os.path.join(folder, type + str(size), f'{frame.id}_{frame.frame_nr}.npz'), uv_body_posed = down) def downscale_uv_maps_posed_no_orient(folder, type, sizes): for size in sizes: os.makedirs(os.path.join(folder, type + '_' + str(size)), exist_ok=True) frames = sorted(os.listdir(os.path.join(folder, type)))[int(args.start):int(args.end)] frames = [Frame(frame.split('_')[0], frame.split('_')[1], frame.split('_')[2].split('.')[0]) for frame in frames] for frame in tqdm.tqdm(frames): img = np.load(os.path.join(folder, type, f'{frame.id}_{frame.frame_nr}_{frame.garment}.npz'))['uv_posed'] img = fill(img, uv_mask_store[512][frame]) for size in sizes: mask = uv_mask_store[size][frame] down = resizers[size](torch.from_numpy(img.transpose((2,0,1))).to(device)).cpu().numpy() down = down * mask[None,:,:] np.savez_compressed(os.path.join(folder, type + '_' + str(size), f'{frame.id}_{frame.frame_nr}_{frame.garment}.npz'), uv_posed = down) def downscale_uv_maps_normals(folder, type, sizes): for size in sizes: os.makedirs(os.path.join(folder, type + '_' + str(size)), exist_ok=True) frames = sorted(os.listdir(os.path.join(folder, type)))[int(args.start):int(args.end)] frames = [Frame(frame.split('_')[0], frame.split('_')[1], frame.split('_')[2].split('.')[0]) for frame in frames] for frame in tqdm.tqdm(frames): img = np.load(os.path.join(folder, type, f'{frame.id}_{frame.frame_nr}_{frame.garment}.npz'))['uv_normals'] img = fill(img, uv_mask_store[512][frame]) for size in sizes: mask = uv_mask_store[size][frame] down = resizers[size](torch.from_numpy(img.transpose((2,0,1))).to(device)).cpu().numpy() down = down * mask[None,:,:] np.savez_compressed(os.path.join(folder, type + '_' + str(size), f'{frame.id}_{frame.frame_nr}_{frame.garment}.npz'), uv_normals = down) def downscale_uv_maps_unposed(folder, type, sizes): for size in sizes: os.makedirs(os.path.join(folder, type + '_' + str(size)), exist_ok=True) frames = sorted(os.listdir(os.path.join(folder, type)))[int(args.start):int(args.end)] frames = [Frame(frame.split('_')[0], frame.split('_')[1], frame.split('_')[2].split('.')[0]) for frame in frames] for frame in tqdm.tqdm(frames): img = np.load(os.path.join(folder, type, f'{frame.id}_{frame.frame_nr}_{frame.garment}.npz'))['uv_unposed'] img = fill(img, uv_mask_store[512][frame]) for size in sizes: mask = uv_mask_store[size][frame] down = resizers[size](torch.from_numpy(img.transpose((2,0,1))).to(device)).cpu().numpy() down = down * mask[None,:,:] np.savez_compressed(os.path.join(folder, type + '_' + str(size), f'{frame.id}_{frame.frame_nr}_{frame.garment}.npz'), uv_unposed = down) def downscale_uv_maps_static(folder, type, sizes): for size in sizes: os.makedirs(os.path.join(folder, type + '_' + str(size)), exist_ok=True) frames = sorted(os.listdir(os.path.join(folder, type))) import random random.shuffle(frames) frames = [(frame.split('_')[0], frame.split('_')[1].split('.')[0]) for frame in frames] for frame in tqdm.tqdm(frames): img = np.load(os.path.join(folder, type, f'{frame[0]}_{frame[1]}.npz'))['uv_static'] img = fill(img, uv_mask_store[512][Frame(frame[0], 0, frame[1])]) for size in sizes: mask = uv_mask_store[size][Frame(frame[0], 0, frame[1])] down = resizers[size](torch.from_numpy(img.transpose((2,0,1))).to(device)).cpu().numpy() down = down * mask[None,:,:] np.savez_compressed(os.path.join(folder, type + '_' + str(size), f'{frame[0]}_{frame[1]}.npz'), uv_static = down) def downscale_uv_maps_mask(folder, type, size): os.makedirs(os.path.join(folder, type + '_' + str(size)), exist_ok=True) frames = sorted(os.listdir(os.path.join(folder, type)))[int(args.start):int(args.end)] for frame in tqdm.tqdm(frames): id, _ = frame.split('.') img = np.load(os.path.join(folder, type, frame)); img = img['uv_mask'].astype(np.float32) down = tf.image.resize(img[:,:,tf.newaxis], size=(size, int(size/2)), method=tf.image.ResizeMethod.NEAREST_NEIGHBOR) down = np.squeeze(down.numpy().astype(bool)) np.savez_compressed(os.path.join(folder, type + '_' + str(size), frame), uv_mask = down) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument("-f", "--folder", help = "end of range", default='/data-local/127/cloth3d_processed') parser.add_argument("-s", "--start", help = "start of range", default=0) parser.add_argument("-e", "--end", help = "end of range", default=25000) parser.add_argument("-n", "--name", help = "end of range", default='lhunor') parser.add_argument("-d", "--debug", help = "end of range", default=False, action='store_true') args = parser.parse_args() # sizes = [32, 64, 128, 256] device = 'cuda' sizes = [32, 64, 128, 256] folder = args.folder resizers = { res: Resize(size=(res, int(res/2)), interpolation=InterpolationMode.BICUBIC).to(device) for res in sizes } resizers_nearest = { res: Resize(size=(res, int(res/2)), interpolation=InterpolationMode.NEAREST_EXACT).to(device) for res in sizes } uv_masks_body = dict() uv_mask_body = np.load('custom_utils/mask_256x512.npy') uv_mask_body = cv2.dilate(uv_mask_body.astype(np.float32), cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=2) uv_masks_body[512] = uv_mask_body.astype(bool) for res in [32, 64, 128, 256]: down = resizers_nearest[res](torch.from_numpy(uv_mask_body[None,:,:].astype(np.int32)).to(device)) down = np.squeeze(down.cpu().numpy().astype(bool)) uv_masks_body[res] = down uv_mask_store = { size: UVMaskStore(folder, size=size) for size in sizes } uv_mask_store[512] = UVMaskStore(folder, size=512) # downscale_uv_maps_mask(folder=folder, type='uv_mask', size=32) # downscale_uv_maps_mask(folder=folder, type='uv_mask', size=64) # downscale_uv_maps_mask(folder=folder, type='uv_mask', size=128) # downscale_uv_maps_mask(folder=folder, type='uv_mask', size=256) # downscale_uv_maps_static(folder=folder, type='uv_static', sizes=sizes) downscale_uv_maps_unposed(folder=folder, type='uv_unposed_no_shape', sizes=sizes) downscale_uv_maps_posed_no_orient(folder=folder, type='uv_posed_no_orient', sizes=sizes) downscale_uv_maps_normals(folder=folder, type='uv_normals', sizes=sizes) downscale_uv_maps_posed_body(folder=folder, type='uv_body_posed_no_orient', sizes=sizes) print()